Data management basics, for UC Davis EDU 292
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This document provides information and guidance about data management for EDU 292. It lists resources for data management from UC Davis Libraries and highlights key reasons for properly managing research data such as reproducibility, credibility, and fulfilling requirements. It discusses metadata, storage options, backups, file formats, and security. It also covers citing data sources accurately and linking works together. The document encourages participants to consider aspects like long-term maintenance, access, and version control for research data and raises questions to facilitate planning proper data management practices.
Data management basics, for UC Davis EDU 292
- 1. Data management for EDU 292 http://data.ucdavis.edu http://guides.lib.ucdavis.edu/education Laura Soito Phoebe Ayers Melissa Browne UC Davis Libraries http://www.slideshare.net/phoebeayers/data- management-martorell-class
- 5. Why should I care? • Reproducibility is a tenet of science. • Research needs to be credible. • You are a good person. • Because you have to. Ideas from presentations by Carly Strasser
- 6. Why should I care? Ideas from presentations by Carly Strasser
- 10. Metadata exercise • Write down all the things you would want to know about a dataset in order to use it. • Then, compare with a partner. Are your lists the same?
- 11. • Title – Name of the dataset or research project that produced it • Creator – Names and addresses of the organization or people who created the data • Identifier – Number used to identify the data, such as project reference number • Dates – Key dates associated with the data, including project start and end date, data modification data release date, and time period covered by the data • Subject – Keywords or phrases describing the subject or content of the data • Funders – Organizations or agencies who funded the research • Rights – Any known intellectual property rights held for the data • Language – Language(s) of the intellectual content of the resource, when applicable • Location – Where the data relates to a physical location, record spatial coverage • Methodology – How the data was generated, including equipment or software used, experimental protocol, other things you might include in a lab notebook From MIT libraries: https://libraries.mit.edu/data- management/store/documentation
- 12. Considerations for data storage • How long? • Who maintains? • Who pays? • Who can access? (and is it findable?)
- 13. Options • Discipline repository (i.e. ICPSR: find a list at http://re3data.org) • General repository (i.e. Harvard Dataverse, Figshare) • Whatever the journal offers • UC repository (Merritt) • DIY (but be careful!)
- 17. More questions to ask • Backups? • What file formats? Compressed? • Security/confidentiality? • Is this the right version?
- 18. Citing data • Tell people how to cite your data • Cite other people’s data accurately • Provide a unique link/ID
- 19. Link up all of your work http://guides.lib.ucdavis.edu/authorid
- 20. Take 1 minute… • What did you learn? • What’s still confusing?
- 21. Thank you Questions? Need help? UC Davis libraries data management group: http://data.ucdavis.edu dataserv@ucdavis.edu